Object Detection in Remote Sensing Images Based on GAN

Luo, Zijuan; Ding, Shuai

doi:10.1145/3349341.3349458

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Thus, the removal of the scan line was done on each band available in 2005-2013 data and the product for each band was exported as a TIFF file in the ArcMap 10.6 environment. Noise reduction, topography correction, radiometric calibration and atmospheric correction using a FLAASH module (Schl€ apfer and Richter 2015; U.S. Environmental Protection Agency 2016; Avtar et al 2019;Luo and Ding 2019). Later, Landsat 7 and 8 images were used as inputs to generate normalized differentiated vegetation index (NDVI), normalized burn ratio (NBR) and delta normalized burn ratio (dNBR) images.…”

Section: Remote Sensing Data and Processingmentioning

confidence: 99%

Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan

Tariq

Hong

Siddiqui

et al. 2021

Geomatics, Natural Hazards and Risk

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The objective of this study is to adopt a methodology for analysing spatial patterns of danger of forest fire at Margalla Hills, Islamabad, Pakistan. The work is concentrated on burnt areas using Landsat data and to classify forest fire severity with different parameters (climatic, vegetation, topography and human activities). In addition to these four variables, the extent of the burned areas was measured. Statistical analysis at each fire scene was used to measure the effect on the variables. To calculate the fire severity ratio correlated to each variable, logistic and stepwise regressions were used. The results showed that the burned areas have increased at a rate of 25.848 ha/day (R 2 ¼ 0.98) if the number of total days since the start of fire has increased. As a result, forest density, distance to roads, average quarterly maximum temperature and average quarterly mean wind speed were highly correlated with the fire severity. Only average quarterly maximum temperature and forest density affected the size of the burnt areas. Prediction maps indicate that 53% of forests are in the very low severity level (0.25-0.45), 25% in the low level (0.45-0.65) and 22% in high and very high levels (>0.65).

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Section: Remote Sensing Data and Processingmentioning

confidence: 99%

Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan

Tariq

Hong

Siddiqui

et al. 2021

Geomatics, Natural Hazards and Risk

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Understanding GANs: fundamentals, variants, training challenges, applications, and open problems

Ahmad,

Jaffri,

Chen

et al. 2024

Multimed Tools Appl

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Identifying Epiphytes in Drones Photos With a Conditional Generative Adversarial Network (C-Gan)

Shashank

Variyar

Sowmya

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Unmanned Aerial Vehicle (UAV) missions often collect large volumes of imagery data. However, not all images will have useful information, or be of sufficient quality. Manually sorting these images and selecting useful data are both time consuming and prone to interpreter bias. Deep neural network algorithms are capable of processing large image datasets and can be trained to identify specific targets. Generative Adversarial Networks (GANs) consist of two competing networks, Generator and Discriminator that can analyze, capture, and copy the variations within a given dataset. In this study, we selected a variant of GAN called Conditional-GAN that incorporates an additional label parameter, for identifying epiphytes in photos acquired by a UAV in forests within Costa Rica. We trained the network with 70%, 80%, and 90% of 119 photos containing the target epiphyte, Werauhia kupperiana (Bromeliaceae) and validated the algorithm’s performance using a validation data that were not used for training. The accuracy of the output was measured using structural similarity index measure (SSIM) index and histogram correlation (HC) coefficient. Results obtained in this study indicated that the output images generated by C-GAN were similar (average SSIM = 0.89–0.91 and average HC 0.97–0.99) to the analyst annotated images. However, C-GAN had difficulty to identify when the target plant was away from the camera, was not well lit, or covered by other plants. Results obtained in this study demonstrate the potential of C-GAN to reduce the time spent by botanists to identity epiphytes in images acquired by UAVs.

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Object Detection in Remote Sensing Images Based on GAN

Cited by 3 publications

References 10 publications

Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan

Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan

Understanding GANs: fundamentals, variants, training challenges, applications, and open problems

Identifying Epiphytes in Drones Photos With a Conditional Generative Adversarial Network (C-Gan)

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